{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# From simulator to Inference with HDDM (LAN version)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Tutorial serves as an example for a full LAN pipeline to inference with HDDM.\n",
    "We start with a *simulator* of a *Sequential Sampling Model* (SSM), use it to generate training data for \n",
    "a *LAN*, train the *LAN* and then use **HDDM** to perform inference on a synthetic dataset from our *simulator*.\n",
    "\n",
    "The example uses the simple DDM for illustration, you can switch out the DDM for your SSM of choice when utilizing this pipeline for your own work.\n",
    "\n",
    "We will make use of two packages in the *LAN ecosystem* (installation instructions below), to help our process. \n",
    "\n",
    "1. The [`ssms`](https://github.com/AlexanderFengler/ssm_simulators) package, which holds a collection of fast simulators and training data generators for LANs\n",
    "2. The [`LANfactory`](https://github.com/AlexanderFengler/LANfactory) package, which makes defining and training LANs convenient. \n",
    "\n",
    "\n",
    "The [`LANfactory`](https://github.com/AlexanderFengler/LANfactory) package is a light-weight convenience package for training `likelihood approximation networks` (LANs) in torch (or keras), \n",
    "starting from supplied training data.\n",
    "\n",
    "[LANs](https://elifesciences.org/articles/65074), although more general in potential scope of applications, were conceived in the context of sequential sampling modeling\n",
    "to account for cognitive processes giving rise to *choice* and *reaction time* data in *n-alternative forced choice experiments* commonly encountered in the cognitive sciences.\n",
    "\n",
    "In this quick tutorial we will use the [`ssms`](https://github.com/AlexanderFengler/ssm_simulators) package to generate our training data using such a sequential sampling model (SSM). The use of of the `LANfactory` package is in no way bound to utilize this `ssms` package.\n",
    "\n",
    "**NOTE**:\n",
    "The [`ssms`](https://github.com/AlexanderFengler/ssm_simulators) package directly generates training data as expected to train **LANs** as per the [`LAN-paper`](https://elifesciences.org/articles/65074)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install (colab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# package to help train networks\n",
    "# !pip install git+https://github.com/AlexanderFengler/LANfactory\n",
    "\n",
    "# package containing simulators for ssms\n",
    "# !pip install git+https://github.com/AlexanderFengler/ssm_simulators\n",
    "\n",
    "# packages related to hddm\n",
    "# !pip install cython\n",
    "# !pip install pymc==2.3.8\n",
    "# !pip install git+https://github.com/hddm-devs/kabuki\n",
    "# !pip install git+https://github.com/hddm-devs/hddm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HDDM\n",
    "import hddm\n",
    "\n",
    "# Package to help train networks (explained above)\n",
    "import lanfactory\n",
    "\n",
    "# Package containing simulators for ssms (explained above)\n",
    "import ssms\n",
    "\n",
    "# Other misc packages\n",
    "import os\n",
    "import numpy as np\n",
    "from copy import deepcopy\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import torch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Training Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configs\n",
    "To create the training data we need, we first specify a configuration dictionary (`generator_config` below), as expected by the `ssms.dataset_generator.data_generator()` function. \n",
    "\n",
    "**NOTE:**\n",
    "Specific to `ssms` package. You can ignore this if you provide your own training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'ddm',\n",
       " 'params': ['v', 'a', 'z', 't'],\n",
       " 'param_bounds': [[-3.0, 0.3, 0.1, 0.0], [3.0, 2.5, 0.9, 2.0]],\n",
       " 'boundary': <function ssms.basic_simulators.boundary_functions.constant(t=0)>,\n",
       " 'n_params': 4,\n",
       " 'default_params': [0.0, 1.0, 0.5, 0.001],\n",
       " 'hddm_include': ['z'],\n",
       " 'nchoices': 2}"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MAKE CONFIGS\n",
    "from ssms.config import data_generator_config\n",
    "\n",
    "# Initialize the generator config (for MLP LANs)\n",
    "\n",
    "# (We start from a supplied example in the ssms package)\n",
    "generator_config = deepcopy(data_generator_config[\"lan\"][\"mlp\"])\n",
    "\n",
    "# Specify generative model (one from the list of included models in the ssms package)\n",
    "generator_config[\"dgp_list\"] = \"ddm\"\n",
    "\n",
    "# Specify number of parameter sets to simulate\n",
    "generator_config[\"n_parameter_sets\"] = 5000\n",
    "\n",
    "# Specify how many samples a simulation run should entail\n",
    "generator_config[\"n_samples\"] = 2000\n",
    "\n",
    "# Specify how many training examples to extract from\n",
    "# a single parametervector\n",
    "generator_config[\"n_training_examples_by_parameter_set\"] = 2000\n",
    "\n",
    "# Specify folder in which to save generated data\n",
    "generator_config[\"output_folder\"] = \"lan_to_hddm_tmp_data/lan_mlp/\"\n",
    "\n",
    "# Make model config dict\n",
    "model_config = ssms.config.model_config[\"ddm\"]\n",
    "\n",
    "# Show\n",
    "model_config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Simulator \n",
    "\n",
    "The `generate_data_training_uniform()` function generates training data as expected by the `LANfactory` package for LAN training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking:  lan_to_hddm_tmp_data/lan_mlp/\n",
      "simulation round: 1  of 10\n",
      "simulation round: 2  of 10\n",
      "simulation round: 3  of 10\n",
      "simulation round: 4  of 10\n",
      "simulation round: 5  of 10\n",
      "simulation round: 6  of 10\n",
      "simulation round: 7  of 10\n",
      "simulation round: 8  of 10\n",
      "simulation round: 9  of 10\n",
      "simulation round: 10  of 10\n",
      "Writing to file:  lan_to_hddm_tmp_data/lan_mlp/training_data_0_nbins_0_n_2000/ddm/training_data_ddm_9ff20d34691a11ed8cc7acde48001122.pickle\n"
     ]
    }
   ],
   "source": [
    "# MAKE DATA\n",
    "my_dataset_generator = ssms.dataset_generators.lan_mlp.data_generator(\n",
    "    generator_config=generator_config, model_config=model_config\n",
    ")\n",
    "\n",
    "training_data = my_dataset_generator.generate_data_training_uniform(save=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Loaders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `LANfactory` package provides some convenience functions to create so-called `data loaders` (finally `DataLoader` `class` in `PyTorch`). These help with making neural network training efficient, when loading training data from file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MAKE DATALOADERS\n",
    "\n",
    "# List of datafiles (here only one)\n",
    "folder_ = \"lan_to_hddm_tmp_data/lan_mlp/training_data_0_nbins_0_n_2000/ddm/\"\n",
    "file_list_ = [folder_ + file_ for file_ in os.listdir(folder_)]\n",
    "\n",
    "# Training dataset\n",
    "torch_training_dataset = lanfactory.trainers.DatasetTorch(\n",
    "    file_IDs=file_list_, batch_size=128\n",
    ")\n",
    "\n",
    "torch_training_dataloader = torch.utils.data.DataLoader(\n",
    "    torch_training_dataset,\n",
    "    shuffle=True,\n",
    "    batch_size=None,\n",
    "    num_workers=1,\n",
    "    pin_memory=True,\n",
    ")\n",
    "\n",
    "# Validation dataset\n",
    "torch_validation_dataset = lanfactory.trainers.DatasetTorch(\n",
    "    file_IDs=file_list_, batch_size=128\n",
    ")\n",
    "\n",
    "torch_validation_dataloader = torch.utils.data.DataLoader(\n",
    "    torch_validation_dataset,\n",
    "    shuffle=True,\n",
    "    batch_size=None,\n",
    "    num_workers=1,\n",
    "    pin_memory=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Network Config\n",
    "\n",
    "`LANfactory` networks take in a `network_config` dictionary, which specifies the *network architecture*.\n",
    "This is necessary to construct our network from the `lanfactory.trainer.TorchMLP` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Network config: \n",
      "{'layer_sizes': [100, 100, 1], 'activations': ['tanh', 'tanh', 'linear'], 'train_output_type': 'logprob'}\n",
      "Train config: \n",
      "{'cpu_batch_size': 128, 'gpu_batch_size': 256, 'n_epochs': 5, 'optimizer': 'adam', 'learning_rate': 0.002, 'lr_scheduler': 'reduce_on_plateau', 'lr_scheduler_params': {}, 'weight_decay': 0.0, 'loss': 'huber', 'save_history': True}\n"
     ]
    }
   ],
   "source": [
    "# SPECIFY NETWORK CONFIGS AND TRAINING CONFIGS\n",
    "\n",
    "network_config = lanfactory.config.network_configs.network_config_mlp\n",
    "\n",
    "print(\"Network config: \")\n",
    "print(network_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To train a network with the `LANfactory` package, we have to specify a `train_config` dictionary, which contains the necessary *training hyperparameters*. In the `config.network_configs` module,  an example is provided, which we slightly adapt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cpu_batch_size': 128,\n",
       " 'gpu_batch_size': 256,\n",
       " 'n_epochs': 5,\n",
       " 'optimizer': 'adam',\n",
       " 'learning_rate': 0.002,\n",
       " 'lr_scheduler': 'reduce_on_plateau',\n",
       " 'lr_scheduler_params': {},\n",
       " 'weight_decay': 0.0,\n",
       " 'loss': 'huber',\n",
       " 'save_history': True}"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lanfactory.config.network_configs import train_config_mlp\n",
    "\n",
    "train_config_mlp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train config: \n",
      "{'cpu_batch_size': 128, 'gpu_batch_size': 256, 'n_epochs': 5, 'optimizer': 'adam', 'learning_rate': 0.002, 'lr_scheduler': 'reduce_on_plateau', 'lr_scheduler_params': {}, 'weight_decay': 0.0, 'loss': 'huber', 'save_history': False}\n"
     ]
    }
   ],
   "source": [
    "train_config = deepcopy(train_config_mlp)\n",
    "train_config[\"save_history\"] = False\n",
    "\n",
    "print(\"Train config: \")\n",
    "print(train_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize Network\n",
    "\n",
    "We can now initialize the network using the `TorchMLP` class provided in the `LANfactory` package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lanfactory.trainers import TorchMLP\n",
    "\n",
    "# LOAD NETWORK\n",
    "net = TorchMLP(\n",
    "    network_config=deepcopy(network_config),\n",
    "    input_shape=torch_training_dataset.input_dim,\n",
    "    save_folder=\"lan_to_hddm_tmp_data/lan_mlp/\",\n",
    "    generative_model_id=\"ddm\",\n",
    ")\n",
    "\n",
    "# SAVE CONFIGS\n",
    "lanfactory.utils.save_configs(\n",
    "    model_id=net.model_id + \"_torch_\",\n",
    "    save_folder=\"lan_to_hddm_tmp_data/lan_mlp/\",\n",
    "    network_config=network_config,\n",
    "    train_config=train_config,\n",
    "    allow_abs_path_folder_generation=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We pass the `train_config` dictionary, our network `net` and the dataloaders to the `ModelTrainerTorchMLP` class from the `LANfactory` package, after which we can train our model with a simple call to the `train_model()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Torch Device:  cpu\n",
      "Found folder:  lan_to_hddm_tmp_data\n",
      "Moving on...\n",
      "Found folder:  lan_to_hddm_tmp_data/lan_mlp\n",
      "Moving on...\n",
      "wandb not available, not storing results there\n",
      "Epoch took 0 / 5,  took 136.11848092079163 seconds\n",
      "epoch 0 / 5, validation_loss: 0.03961\n",
      "Epoch took 1 / 5,  took 113.36870789527893 seconds\n",
      "epoch 1 / 5, validation_loss: 0.04193\n",
      "Epoch took 2 / 5,  took 112.76040506362915 seconds\n",
      "epoch 2 / 5, validation_loss: 0.03371\n",
      "Epoch took 3 / 5,  took 109.81652188301086 seconds\n",
      "epoch 3 / 5, validation_loss: 0.03548\n",
      "Epoch took 4 / 5,  took 111.44894003868103 seconds\n",
      "epoch 4 / 5, validation_loss: 0.03456\n",
      "Saving model state dict\n",
      "Training finished successfully...\n"
     ]
    }
   ],
   "source": [
    "# TRAIN MODEL\n",
    "model_trainer = lanfactory.trainers.ModelTrainerTorchMLP(\n",
    "    train_config=train_config,\n",
    "    data_loader_train=torch_training_dataloader,\n",
    "    data_loader_valid=torch_validation_dataloader,\n",
    "    model=net,\n",
    "    output_folder=\"lan_to_hddm_tmp_data/lan_mlp/\",\n",
    ")\n",
    "\n",
    "model_trainer.train_model(save_history=False, save_model=True, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use in HDDM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We generated training data from model simulations, and trained our LAN.\n",
    "\n",
    "Let's proceed to use our freshly minted LAN in HDDM for inference on our *custom model* (as a reminder, this was mundanely just a DDM for purposes of this tutorial)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define HDDM Model Config\n",
    "\n",
    "The `HDDMnn()` classes generally expect a `model_config` dictionary which specifies details about parameters names, allowed parameters values and other aspects of a given SSM. \n",
    "\n",
    "If a valid *model string* is provided as an argument, HDDM will supply the appropriate `model_config` (and respective LAN) internally from the model bank that is already included in the package. \n",
    "\n",
    "Instead, we can supply a custom `model_config`, as well as a custom *likelihood* (via the `network` argument), with very few a priori restrictions. \n",
    "\n",
    "We will now define such a custom `model_config` and then show how to provide our LAN trained above as a custom likelihood, after which we can sample from our custom HDDM model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_model_config = {}\n",
    "\n",
    "# Parameter names associated to your model\n",
    "my_model_config[\"params\"] = [\"v\", \"a\", \"z\", \"t\"]\n",
    "\n",
    "# The parameter boundaries you used for training your LAN\n",
    "my_model_config[\"param_bounds\"] = [[-3.0, 0.3, 0.1, 1e-3], [3.0, 2.5, 0.9, 2.0]]\n",
    "\n",
    "# Suggestion for which parameters to include\n",
    "# via the include statement of an HDDM model\n",
    "# (usually you want all of the parameters from above)\n",
    "my_model_config[\"hddm_include\"] = [\"v\", \"a\", \"z\", \"t\"]\n",
    "\n",
    "# choice labels (what your simulator spits out)\n",
    "my_model_config[\"choices\"] = [-1, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specifies parameters which the sampler should tansform (optional)\n",
    "my_model_config[\"params_trans\"] = [0, 0, 0, 0]\n",
    "\n",
    "# adds sampler settings for each parameter\n",
    "# (optional: Can improve sampler speed if informed decision made here)\n",
    "my_model_config[\"slice_widths\"] = {\n",
    "    \"v\": 1.5,\n",
    "    \"v_std\": 1,\n",
    "    \"a\": 1,\n",
    "    \"a_std\": 1,\n",
    "    \"z\": 0.1,\n",
    "    # \"z_trans\": 0.2,\n",
    "    \"z_std\": 0.2,\n",
    "    \"t\": 0.01,\n",
    "    \"t_std\": 0.15,\n",
    "}\n",
    "\n",
    "# Default values for parameters\n",
    "# (Useful if you don't intend to fit one or more of them)\n",
    "my_model_config[\"params_default\"] = [0.0, 1.0, 0.5, 1e-3]\n",
    "\n",
    "# Set a (reasonable) upper limit of group level standard deviations,\n",
    "# (optional: Can help with sampler stability)\n",
    "my_model_config[\"params_std_upper\"] = [1.5, 1.0, None, 1.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Network \n",
    "\n",
    "The `LoadTorchMLPInfer()` function is used to load a network in inference mode. We explain more below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tanh\n",
      "tanh\n",
      "linear\n"
     ]
    }
   ],
   "source": [
    "from lanfactory.trainers import LoadTorchMLPInfer\n",
    "\n",
    "net = LoadTorchMLPInfer(\n",
    "    model_file_path=\"lan_to_hddm_tmp_data/lan_mlp/\"\n",
    "    + \"2d4eedae67b911ed8acaacde48001122_ddm_torch_state_dict.pt\",\n",
    "    network_config=network_config,\n",
    "    input_dim=6,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `LoadTorchMLPInfer()` class loads our network in `eval` mode and stops gradients from being accumulated. \n",
    "Importantly, it exposes a `predict_on_batch()` method, which is what HDDM will call internally. \n",
    "\n",
    "The naming of this function is a left-over from `keras` days, however what it does internally may be important if you would like to supply a fully custom likelihood at some point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A look at the internals\n",
    "from lanfactory.trainers import TorchMLP\n",
    "\n",
    "\n",
    "class LoadTorchMLPInfer:\n",
    "    def __init__(self, model_file_path=None, network_config=None, input_dim=None):\n",
    "        torch.backends.cudnn.benchmark = True\n",
    "        self.dev = (\n",
    "            torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "        )\n",
    "        self.model_file_path = model_file_path\n",
    "        self.network_config = network_config\n",
    "        self.input_dim = input_dim\n",
    "\n",
    "        self.net = TorchMLP(\n",
    "            network_config=self.network_config,\n",
    "            input_shape=self.input_dim,\n",
    "            generative_model_id=None,\n",
    "        )\n",
    "        self.net.load_state_dict(torch.load(self.model_file_path))\n",
    "        self.net.to(self.dev)\n",
    "        self.net.eval()\n",
    "\n",
    "    @torch.no_grad()\n",
    "    def __call__(self, x):\n",
    "        return self.net(x)\n",
    "\n",
    "    @torch.no_grad()\n",
    "    def predict_on_batch(self, x=None):\n",
    "        return self.net(torch.from_numpy(x).to(self.dev)).cpu().numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The argument `x`, to the `predict_on_batch()`, when called from within HDDM's sampler, will be a matrix. \n",
    "Rows correspond to trials, and columns are supplied in the following way. \n",
    "\n",
    "The first few columns contain trial wise parameters (in the order specific in the `model_config` above under the `\"params\"` `key`).\n",
    "The last two columns contain the trial wise *reaction times* and *choices* respectively.\n",
    "\n",
    "To understand better how HDDM calls such a custom likelihood *internally*, see the code below.\n",
    "\n",
    "**NOTE:**\n",
    "Don't run the cell below, it is just for illustration!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wiener_like_nn_mlp_pdf(np.ndarray[float, ndim = 1] rt,\n",
    "                           np.ndarray[float, ndim = 1] response,\n",
    "                           np.ndarray[float, ndim = 1] params,\n",
    "                           double p_outlier = 0, \n",
    "                           double w_outlier = 0,\n",
    "                           bint logp = 0,\n",
    "                           network = None):\n",
    "    \n",
    "    cdef Py_ssize_t size = rt.shape[0]\n",
    "    cdef Py_ssize_t n_params = params.shape[0]\n",
    "\n",
    "    cdef np.ndarray[float, ndim = 1] log_p = np.zeros(size, dtype = np.float32)\n",
    "    cdef float ll_min = -16.11809\n",
    "\n",
    "    cdef np.ndarray[float, ndim = 2] data = np.zeros((size, n_params + 2), dtype = np.float32)\n",
    "    data[:, :n_params] = np.tile(params, (size, 1)).astype(np.float32)\n",
    "    data[:, n_params:] = np.stack([rt, response], axis = 1)\n",
    "\n",
    "    # Call to network:\n",
    "    if p_outlier == 0: \n",
    "        log_p = np.squeeze(np.core.umath.maximum(network.predict_on_batch(data), ll_min))\n",
    "    else: \n",
    "        log_p = np.squeeze(np.log(np.exp(np.core.umath.maximum(network.predict_on_batch(data), ll_min)) * (1.0 - p_outlier) + (w_outlier * p_outlier)))\n",
    "    if logp == 0:\n",
    "        log_p = np.exp(log_p)\n",
    "    return log_p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the *internal likelihood function* expects a network as input and then finally calls the `predict_on_batch()` to get log-likelihoods. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Example Dataset\n",
    "\n",
    "We can now generate an example dataset and use our newly created LAN to fit our custom DDM to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rt</th>\n",
       "      <th>response</th>\n",
       "      <th>subj_idx</th>\n",
       "      <th>v</th>\n",
       "      <th>a</th>\n",
       "      <th>z</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.133998</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.774994</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.028006</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.188997</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.822996</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>1.944002</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>1.384995</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>0.967000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>3.996943</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>1.320996</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           rt  response  subj_idx    v    a     z    t\n",
       "0    1.133998       1.0         0  0.9  1.4  0.45  0.7\n",
       "1    1.774994       1.0         0  0.9  1.4  0.45  0.7\n",
       "2    2.028006       1.0         0  0.9  1.4  0.45  0.7\n",
       "3    1.188997       1.0         0  0.9  1.4  0.45  0.7\n",
       "4    1.822996       1.0         0  0.9  1.4  0.45  0.7\n",
       "..        ...       ...       ...  ...  ...   ...  ...\n",
       "495  1.944002       1.0         0  0.9  1.4  0.45  0.7\n",
       "496  1.384995       1.0         0  0.9  1.4  0.45  0.7\n",
       "497  0.967000       1.0         0  0.9  1.4  0.45  0.7\n",
       "498  3.996943       1.0         0  0.9  1.4  0.45  0.7\n",
       "499  1.320996       1.0         0  0.9  1.4  0.45  0.7\n",
       "\n",
       "[500 rows x 7 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose some parameters\n",
    "v = 0.9\n",
    "a = 1.4\n",
    "z = 0.45\n",
    "t = 0.7\n",
    "\n",
    "# Simulate Data\n",
    "data = ssms.basic_simulators.simulator(model=\"ddm\", theta=[v, a, z, t], n_samples=500)\n",
    "\n",
    "# Bring into correct shape expected from HDDM\n",
    "data_df = pd.DataFrame(\n",
    "    np.stack([data[\"rts\"], data[\"choices\"]], axis=1)[:, :, 0],\n",
    "    columns=[\"rt\", \"response\"],\n",
    ")\n",
    "data_df[\"subj_idx\"] = 0\n",
    "data_df[\"v\"] = v\n",
    "data_df[\"a\"] = a\n",
    "data_df[\"z\"] = z\n",
    "data_df[\"t\"] = t\n",
    "\n",
    "\n",
    "data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the RTs\n",
    "plt.hist(\n",
    "    data_df[\"rt\"] * data_df[\"response\"],\n",
    "    histtype=\"step\",\n",
    "    color=\"black\",\n",
    "    density=True,\n",
    "    bins=30,\n",
    ")\n",
    "plt.xlabel(\"Reaction Time\")\n",
    "plt.ylabel(\"Density\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define HDDM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Supplied model_config specifies params_std_upper for  z as  None.\n",
      "Changed to 10\n"
     ]
    }
   ],
   "source": [
    "# Define the HDDM model\n",
    "hddmnn_model = hddm.HDDMnn(\n",
    "    data=data_df,\n",
    "    informative=False,\n",
    "    include=my_model_config[\"hddm_include\"],\n",
    "    model_config=my_model_config,\n",
    "    network=net,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 1000 of 1000 complete in 71.1 sec"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<pymc.MCMC.MCMC at 0x14af06150>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hddmnn_model.sample(1000, burn=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ground_truth</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>0.90</td>\n",
       "      <td>1.001513</td>\n",
       "      <td>0.065515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.40</td>\n",
       "      <td>1.327698</td>\n",
       "      <td>0.056254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>0.45</td>\n",
       "      <td>0.435518</td>\n",
       "      <td>0.022863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.765093</td>\n",
       "      <td>0.041211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ground_truth      mean       std\n",
       "v          0.90  1.001513  0.065515\n",
       "a          1.40  1.327698  0.056254\n",
       "z          0.45  0.435518  0.022863\n",
       "t          0.70  0.765093  0.041211"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = hddmnn_model.gen_stats()\n",
    "tmp[\"ground_truth\"] = data_df.iloc[0, 3:]\n",
    "tmp[[\"ground_truth\", \"mean\", \"std\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We show two plots. *First*, we compare simulations fixing the parameters at the posterior mean with the original data, to get a visual idea of the model fit we obtained. *Second* we show the posterior traces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare simulations from posterior mean parameters\n",
    "# to original data\n",
    "data_post_mean = data = ssms.basic_simulators.simulator(\n",
    "    model=model, theta=list(tmp[\"mean\"].values), n_samples=500\n",
    ")\n",
    "\n",
    "\n",
    "# Plotting the RTs\n",
    "plt.hist(\n",
    "    data_df[\"rt\"] * data_df[\"response\"],\n",
    "    histtype=\"step\",\n",
    "    color=\"black\",\n",
    "    density=True,\n",
    "    bins=30,\n",
    "    label=\"Original Data\",\n",
    ")\n",
    "plt.hist(\n",
    "    data_post_mean[\"rts\"] * data_post_mean[\"choices\"],\n",
    "    histtype=\"step\",\n",
    "    color=\"red\",\n",
    "    density=True,\n",
    "    bins=30,\n",
    "    label=\"Posterior Mean\",\n",
    ")\n",
    "plt.xlabel(\"Reaction Time\")\n",
    "plt.ylabel(\"Density\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting v\n",
      "Plotting a\n",
      "Plotting z\n",
      "Plotting t\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "hddmnn_model.plot_posteriors()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**END**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "hddmnn_tutorial",
   "language": "python",
   "name": "hddmnn_tutorial"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7 (default, May  6 2020, 04:59:01) \n[Clang 4.0.1 (tags/RELEASE_401/final)]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "ac2eaa0ea0ebeafcc7822e65e46aa9d4f966f30b695406963e145ea4a91cd4fc"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
